An Empirical Comparison of Neural Architectures for Reinforcement Learning in Partially Observable Environments

Denis Steckelmacher; Peter Vrancx

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arXiv · 1512.05509

An Empirical Comparison of Neural Architectures for Reinforcement Learning in Partially Observable Environments

Denis Steckelmacher·Peter Vrancx

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Abstract

This paper explores the performance of fitted neural Q iteration for reinforcement learning in several partially observable environments, using three recurrent neural network architectures: Long Short-Term Memory, Gated Recurrent Unit and MUT1, a recurrent neural architecture evolved from a pool of several thousands candidate architectures. A variant of fitted Q iteration, based on Advantage values instead of Q values, is also explored. The results show that GRU performs significantly better than LSTM and MUT1 for most of the problems considered, requiring less training episodes and less CPU time before learning a very good policy. Advantage learning also tends to produce better results.

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BibTeX RIS

Denis Steckelmacher, Peter Vrancx. 2015-12-17. An Empirical Comparison of Neural Architectures for Reinforcement Learning in Partially Observable Environments. https://arxiv.org/abs/1512.05509

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